基于HOG特征协方差矩阵的动作识别算法

为了提高HOG在动作识别应用中的识别率,提出一种基于HOG特征协方差矩阵的动作识别算法。算法以HOG特征为样本计算协方差矩阵,再通过矩阵对数运算,将协方差矩阵从黎曼流形映射到线性空间;然后从协方差矩阵中提取描述子,基于不同的核函数使用支持向量机进行分类。在公开的三个数据库Weizmann,KTH和UCF Sports上分别进行实验,实验结果表明,文中提出的算法具有很好的识别性能。     

基于泰森多边形特征分解的人脸识别

针对在复杂背景下传统的人脸识别方法存在算法复杂、鲁棒性差以及精确度低等不足,提出一种基于泰森多边形特征分解的人脸识别算法.首先建立高斯肤色模型并融合人脸几何特征实现粗定位;然后,根据人脸区域各特征部位的特性对人脸特征点定位同时采用两次泰森多边形进行特征分割,使每个特征点分割到各自的特征区域内,从而有利于形成编码;最后,运用LBP算子对多尺度多方向的Log-Gabor幅值信息进行纹理描述,并统计其分布规律.实验结果表明,该算法简单,鲁棒性强,具有较高的识别精度.     

基于面部几何特征点提取的人脸识别方法

文中介绍了一种通过提取特征点信息进行人脸识别的方法。在利用形态交离变换确定眼球位置的基础上,根据区域点投影曲线检测特征点,然后构造尺寸、位移、旋转不变的特征向量,并与样本库中的人脸特征向量相比计算其相似度,依据相似的程度完成识别。实践证明该方法对人脸正面图具有准确的识别率,并具有强抗干扰能力。     

基于稀疏表示与特征融合的人脸识别方法

针对人脸识别在有遮挡、表情、光照的变化或受到噪声污染时鲁棒性变差问题,提出一种基于稀疏表示与特征融合的人脸识别算法。首先采用低秩恢复算法得到训练样本和测试样本的干净人脸图像,提取干净人脸图像的LBP,HOG,Gabor三种特征向量;然后对部分训练样本进行SRC分类测试,根据SRC的识别结果与分类残差定义一个损失函数,再利用正则化最小二乘法计算出使损失函数最小的权重向量;最后根据该权重向量重构规则化残差进行分类。在ORL,Extended Yale B和AR数据库上进行实验,结果表明,该算法优于利用单一特征识别的方法,并且对光照、噪声、遮挡等因素产生的影响有较好的泛化性能。     

基于区域协方差矩阵特征与流形SVDD的SAR目标鉴别

为提高合成孔径雷达自动目标鉴别方法中鉴别特 征的可分性及鉴别器的 拒判性能,提出了一种基于区域协方差矩阵特征与一种迭代的SVDD相结合的目 标鉴别方法。融合多种纹理特征及其相关性,构造了一种基于区域协方差矩阵的 鉴别特征,该特征在实验中取得了良好的可分性且无需进一步的特征选择。将 SVDD的分类准则与协方差矩阵特征空间的流形结构相结合,设计了一种迭代流 形SVDD鉴别器,通过一种新的迭代方法选择SVDD的超球面中心代替Karcher均值点作为映射 基点。在RADARSAT-2实测数据上的实验结果验证了新方法的有效性。     

基于特征点邻域SIFT描述子的人脸识别

在人脸识别算法中,尺度不变的SIFT特征是人脸局部特征的重要描述方式之一。在传统SIFT方法中,由于特征点的坐标是根据高斯差分空间的局部极值点来确定的,造成特征点匹配困难。通过基于回归的局部二值特征对人脸进行校准,确定对人脸有意义的特征点位置。用SIFT特征描述子的不变特性描述人脸的局部特征,能够有效地提高识别速度以及识别率。对特征点进行区域加权,能够对人脸的姿态变化以及角度偏转有一定的鲁棒性。     

Pattern recognition using discriminative feature extraction

We propose a new design method, called discriminative feature extraction for practical modular pattern recognizers. A key concept of discriminative feature extraction is the design of an overall recognizer in a manner consistent with recognition error minimization. The utility of the method is demonstrated in a Japanese vowel recognition task     

Face recognition algorithm using local and global information

A successful algorithm for the recognition of partially distorted human face images is presented. The key to this approach is to combine both local and global information. The results are compared with traditional elastic matching algorithms and a decreased mismatch rate is reported     

Real-time vehicle recognition using local feature extraction

A novel feature extraction method based on local textures of images for real-time vehicle recognition tasks is proposed. In particular, feature vectors are extracted from judicious combinations of partitioning and overlapping image blocks with a view to reducing cardinality while retaining sufficient feature information for effective recognition. Experimental results are presented that show a reduction in computational load of 80% and robustness to illumination and noise can be achieved     

Face recognition using Two Dimensional Laplacian EigenMap

Recently, some research efforts have shown that face images possibly reside on a nonlinear sub-manifold. Though Laplacianfaees method considered the manifold structures of the face images, it has limits to solve face recognition problem. This paper proposes a new feature extraction method, Two Dimensional Laplacian EigenMap （2DLEM）, which especially considers the manifold structures of the face images, and extracts the proper features from face image matrix directly by using a linear transformation. As opposed to Laplacianfaces, 2DLEM extracts features directly from 2D images without a vectorization preprocessing. To test 2DLEM and evaluate its performance, a series of ex- periments are performed on the ORL database and the Yale database. Moreover, several experiments are performed to compare the performance of three 2D methods. The experiments show that 2DLEM achieves the best performance.     

分块双向2 DPCA融合局部特征的人脸识别

针对(2D)2PCA无法保存某些重要局部特征的问题,提出了一种分块双向二维主成分分析融合局部特征方法。首先,将图像分解为互不重叠的子块,每个子块包含重要的局部信息,利用(2D)2PCA对子块进行特征提取并投影到特征子空间;然后,对每个子块分别设计一个分类器并在一定置信度范围内判别测试样本所属类别。最后,根据所有子块所属类别的置信度之和完成人脸分类。在四个人脸识别数据库上的实验结果表明,相比其它几种人脸识别算法,所提方法取得了更高的识别精度。     

Face recognition using spatially constrained earth mover's distance

Face recognition is a challenging problem, especially when the face images are not strictly aligned (e.g., images can be captured from different viewpoints or the faces may not be accurately cropped by a human or automatic algorithm). In this correspondence, we investigate face recognition under the scenarios with potential spatial misalignments. First, we formulate an asymmetric similarity measure based on Spatially constrained Earth Mover's Distance (SEMD), for which the source image is partitioned into nonoverlapping local patches while the destination image is represented as a set of overlapping local patches at different positions. Assuming that faces are already roughly aligned according to the positions of their eyes, one patch in the source image can be matched only to one of its neighboring patches in the destination image under the spatial constraint of reasonably small misalignments. Because the similarity measure as defined by SEMD is asymmetric, we propose two schemes to combine the two similarity measures computed in both directions. Moreover, we adopt a distance-as-feature approach by treating the distances to the reference images as features in a Kernel Discriminant Analysis (KDA) framework. Experiments on three benchmark face databases, namely the CMU PIE, FERET, and FRGC databases, demonstrate the effectiveness of the proposed SEMD.     

Face recognition using message passing based clustering method

Traditional subspace analysis methods are inefficient and tend to be affected by noise as they compare the test image to all training images, especifically when there are large numbers of training images. To solve such problem, we propose a fast face recognition (FR) technique called APLDA by combining a novel clustering method affinity propagation (AP) with linear discriminant analysis (LDA). By using AP on the reduced features derived from LDA, a representative face image for each subject can be reached. Thus, our APLDA uses only the representative images rather than all training images for identification. Obviously, APLDA is much more computationally efficient than Fisherface. Also, unlike Fisherface who uses pattern classifier for identification, APLDA performs the identification using AP once again to cluster the test image into one of the representative images. Experimental results also indicate that APLDA outperforms Fisherface in terms of recognition rate.     

基于稠密轨迹特征的红外人体行为识别

提出了一种使用基于稠密轨迹(DT)融合特征的红外人体行为识别(HAR)方法。主要流程如下:1)通过稠密采样获得输入行为视频的DT;2)计算DT的方向梯度直方图(HOG)、光流直方图(HOF)和运动边界描述子(MBH)3个描述子;3)基于DT的HOG、HOF和MBH,并采取词袋库模型和一定的融合策略,构建融合特征;4)以第3步所构建的融合特征为k近邻分类器(k-NN)的输入,完成人体HAR。实验以IADB红外行为库为研究对象,正确识别率达到96.7%。结果表明,提出的特征融合及识别方法能有效地对红外人体行为进行识别。     

基于局部SIFT分析的手背静脉识别

针对新兴的手背静脉识别技术,提出了一种具有位移和旋转不变性的局部尺度不变特征(SIFT,scale in-variant feature transform)分析方法.首先确定手背静脉图像的感兴趣区域(ROI)并对其进行滤波去噪,然后提取手背静脉血管的SIFT并对特征点进行匹配,最后计算注册样本和待识别样本的特征匹配率并以此作为相似性测度进行身份识别.利用我们建立的手背静脉血管图像数据库对该算法进行了性能测试,并与目前最典型的识别方法进行了对比.实验结果表明,本算法具有更好的识别性能,其中识别速度得到了很大的提高.     

Face recognition using recursive Fisher linear discriminant.

Fisher linear discriminant (FLD) has recently emerged as a more efficient approach for extracting features for many pattern classification problems as compared to traditional principal component analysis. However, the constraint on the total number of features available from FLD has seriously limited its application to a large class of problems. In order to overcome this disadvantage, a recursive procedure of calculating the discriminant features is suggested in this paper. The new algorithm incorporates the same fundamental idea behind FLD of seeking the projection that best separates the data corresponding to different classes, while in contrast to FLD the number of features that may be derived is independent of the number of the classes to be recognized. Extensive experiments of comparing the new algorithm with the traditional approaches have been carried out on face recognition problem with the Yale database, in which the resulting improvement of the performances by the new feature extraction scheme is significant.     

Face recognition using the weighted fractal neighbor distance

We present a method for performing face recognition based on the fractal neighbor distance (FND). The FND has previously been used for face recognition. What distinguishes our method from others is that we incorporate the use of localized weights with the FND. In a local-to-global feature matching approach, a set of localized weights is used with an algorithm based on the FND that searches for local features. A global score is then derived from each localized score. This set of weights is designed to concentrate around the eyes and nose region of the face, because they contain more discriminating features.     

融合CDI和LBP的人脸特征提取与识别算法

针对光照、姿态和表情对人脸识别率造成严重影响的问题,提出了结合笛卡儿微分不变量(CDI,cartesian differential invariant)和LBP(local binary patterns)的人脸特征抽取与识别算法。首先,利用高斯微分算子抽取人脸图像的微分结构,组合这些微分结构得到一个不可约简的笛卡儿CDI集。其次,对CDI集中每个分量分别计算其LBP特征,并将所有分量的LBP特征连接起来以得到人脸图像的特征。最后,运用所抽取出的人脸局部描述特征和支持向量机(SVM)分类器完成人脸图像分类与识别。试验分析表明,基于CDI的LBP特征对人脸位置、姿态、光照和表情的变化具有较高的不变性。该算法在ORL和Yale人脸库中分别取得了98.5%和98.89%的识别率。